CN113780922A

CN113780922A - Goods flow direction and flow rate determining method and device

Info

Publication number: CN113780922A
Application number: CN202110057394.4A
Authority: CN
Inventors: 沈婧楠
Original assignee: Beijing Jingdong Qianshi Technology Co Ltd
Current assignee: Beijing Jingdong Qianshi Technology Co Ltd
Priority date: 2021-01-15
Filing date: 2021-01-15
Publication date: 2021-12-10

Abstract

The invention discloses a method and a device for determining the flow direction and the flow rate of goods, and relates to the technical field of warehouse logistics. One embodiment of the method comprises: representing the processes of warehousing, storing and ex-warehousing of goods as a network structure diagram consisting of nodes and directed edges, further generating a network structure table comprising source nodes and corresponding destination nodes, acquiring preset warehouse data, basic attribute data of the goods, storage requirement data of the goods and preset configuration parameter data, and generating a network flow model and constraints and targets of the network flow model according to the information of each node in the network structure table and the acquired data; and model solving is carried out on the network flow model based on the constraint and the target, and then the flow direction and the flow distribution of the goods are determined. The method and the system realize intelligent recommendation of the flow direction and the flow of goods, reduce the cost and improve the efficiency.

Description

Goods flow direction and flow rate determining method and device

Technical Field

The invention relates to the field of warehouse logistics, in particular to a method and a device for determining the flow direction and the flow rate of goods.

Background

The most basic functions of a warehouse are the entry and exit and storage of items, and furthermore, also involve the sorting, packaging and other additional functions of the goods. A large and medium-sized warehouse often has a plurality of storage areas, and warehousing, ex-warehouse and transfer (replenishment) of goods between different storage areas require the use of specific equipment and manual operation. When goods are put in storage, a proper storage area needs to be recommended for the goods according to the storage area attribute and the goods attribute.

In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:

at present, the warehousing flow direction (and flow rate) of goods is judged based on experience or basic attributes (size, ex-warehouse frequency, storage requirements and the like) of the goods, and no intelligent recommendation method aiming at the warehousing flow direction (and flow rate) of the goods is provided.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method and an apparatus for determining flow direction and flow rate of goods, which abstract each region, device, and operation flow of a warehouse into a network graph formed by nodes and directed edges, plan a suitable storage area, flow rate and flow direction of entering and exiting the warehouse for each goods through a network flow model, optimize overall cost on the premise of meeting the needs of entering and exiting the warehouse and storing business, improve overall efficiency, and implement intelligent recommendation.

To achieve the above object, according to a first aspect of embodiments of the present invention, there is provided a method for determining a flow direction and a flow rate of goods, including:

the processes of warehousing, storing and ex-warehousing goods are represented as a network structure diagram consisting of nodes and directed edges, so as to generate a network structure table comprising source nodes and corresponding destination nodes,

acquiring preset warehouse data, basic attribute data of goods, storage requirement data of the goods and preset configuration parameter data,

generating a network flow model and constraints and targets of the network flow model according to the information of each node in the network structure table, the acquired warehouse data, the basic attribute data of the goods, the storage requirement data of the goods and the configuration parameter data; and

and carrying out model solution on the network flow model based on the constraint and the target so as to determine the flow direction and flow distribution of the goods.

Preferably, in the method for determining a flow direction and a flow rate of goods in the first aspect, before generating the constraints and the targets of the network flow model, the method further includes the following steps:

generating, in conjunction with the warehouse data, a node information table showing at least node names for the nodes contained in the network configuration table and node functions indicating whether or not a node can store;

generating an item information table showing basic attribute data of each item according to the basic attribute data of the item;

generating a storage policy table showing a storage policy of each kind of goods at a corresponding node name in the node information table according to the storage requirement data of the goods, an

Generating a configuration parameter table showing parameter names and corresponding parameter values according to preset configuration parameter data, and

and generating a table, the node information table, the goods information table, the storage strategy table and the configuration parameter table according to the network structure diagram, and generating the network flow model and the constraint and target of the network flow model.

Preferably, in the method for determining a flow direction and a flow rate of goods in the first aspect, the generating of the constraints and the targets of the network flow model includes the following steps:

the network flow model is divided into a first stage to a third stage, wherein the first stage flows into the safety stock, the second stage flows into and out of the turnover stock and meets the demand of sales in a period, the third stage flows out of the safety stock,

the model parameters of the model are calculated,

the model variables of the model are generated and,

model constraints for generating a model, an

A model object of the model is generated.

Preferably, in the method for determining a flow direction and a flow rate of goods in the first aspect, the obtaining of the basic attribute data of the goods includes obtaining information data of the goods, and warehousing and inventory data of the goods in a predetermined period of time in the past; and obtaining the warehouse data comprises obtaining one or more of a storage capacity, a storage cost, a traffic capacity, and a traffic cost of the warehouse.

Preferably, in the method for determining the flow direction and the flow rate of the goods in the first aspect, the configuration parameter table includes one or more of a storage period, a predetermined unit of working time, a storage volume coefficient, and a period cost coefficient, and corresponding parameter values.

Preferably, in the method for determining the flow direction and the flow rate of the goods in the first aspect, the network structure diagram is generated by using the following nodes connected in sequence by directed edges: warehousing, storing, picking and delivering goods; warehousing, goods-to-person storage, goods-to-person selection and ex-warehouse; or the network structure diagram is generated by the following nodes which are sequentially connected by the directed edges: warehousing, vertical warehouse storage area keeping, stacker, vertical warehouse zero picking area, 1 st picking and warehouse discharging; warehousing, vertical warehouse storage area protection, stacker, 2 nd forklift, flat warehouse zero picking area, 2 nd picking and warehouse discharging; warehousing, a flat warehouse zero picking area, 2 nd picking and ex-warehouse; warehousing, erecting a zero-picking area, 1 st picking and delivering; and warehousing, a flat warehouse storage area, a 1 st forklift, a flat warehouse zero-picking area, a 2 nd picking and ex-warehouse.

Preferably, in the method for determining a flow direction and a flow rate of goods in the first aspect, the node information table includes a node name of each node, a node function indicating whether the node is a storable node, an upper limit of a storage capacity, a storage cost, an online capacity of a flow rate, and a flow rate cost.

Preferably, in the method for determining a flow rate of an article flowing to the first aspect, the article information table includes an article name of each article, a unit volume of the article, a low stock/high stock number of the article, and an article sales amount.

Preferably, in the method for determining a flow rate of goods in the first aspect, the storage policy table includes a name of each goods, a node name, and a storage requirement, the storage requirement of the goods includes a minimum number of storage items, a maximum number of storage items, and a turnaround time in the case of stock, and the storage requirement is a number greater than zero or is empty, and when the storage requirement is empty, it indicates that there is no constraint on the storage requirement.

To achieve the above object, according to a second aspect of the embodiments of the present invention, there is provided a goods flow direction flow rate determination device including:

a network structure table generation module: the process of warehousing, storing and ex-warehousing the goods is represented as a network structure diagram consisting of nodes and directed edges, and a network structure table comprising source nodes and corresponding destination nodes is further generated;

a data acquisition module: it acquires preset warehouse data, basic attribute data of goods, storage requirement data of the goods and preset configuration parameter data,

a network flow model generation module which generates a network flow model according to the information of each node in the network structure table generated by the network structure table generation module, the warehouse data, the basic attribute data of the goods, the storage requirement data of the goods and the configuration parameter data acquired by the data acquisition module, and generates the constraint and the target of the network flow model; and

a calculation module: model solution is performed on the network flow model based on the constraints and the targets, and then flow direction and flow distribution of the goods are determined.

Preferably, in the goods flow direction and flow rate determining device according to the second aspect, the network flow model generating module further performs the following operations before generating the network flow model and its constraints and targets:

generating, in conjunction with the warehouse data, a node information table showing at least node names for the nodes contained in the network configuration table and node functions indicating whether or not the nodes can store;

the network flow model generation module generates a table, the node information table, the goods information table, the storage strategy table and the configuration parameter table according to the network structure diagram, and generates the network flow model and the constraint and target of the network flow model.

Preferably, in the goods flow direction flow rate determination device according to the second aspect, the network flow model generation module performs the following operations:

dividing the generated network flow model into a first stage to a third stage, wherein the first stage flows into the safety stock, the second stage flows into and out of the turnover stock and meets the demand of sales in a period, the third stage flows out of the safety stock,

the model parameters of the model are calculated,

the model variables of the model are generated and,

model constraints for generating a model, an

A model object of the model is generated.

Preferably, in the flow rate determining device for the goods flow of the second aspect, the basic attribute data of the goods acquired by the data acquiring module includes information data of the goods, and warehousing and inventory data of the goods in a predetermined period of time in the past; and is

The warehouse data acquired by the data acquisition module comprises one or more of storage capacity, storage cost, flow capacity and flow cost of a warehouse.

Preferably, in the device for determining the flow rate of goods flowing to the second aspect, the configuration parameter data acquired by the data acquiring module includes one or more of a storage period, a predetermined unit of working time, a storage volume coefficient, and a period cost coefficient, and a corresponding parameter value.

Preferably, in the goods flow direction and flow rate determining device according to the second aspect, the network structure table generating module generates the network structure diagram by using the following nodes sequentially connected by a directed edge: warehousing, storing, picking and delivering goods; warehousing, goods-to-person storage, goods-to-person selection and ex-warehouse; or

Generating the network structure diagram by using the following nodes which are sequentially connected by directed edges: warehousing, vertical warehouse storage area keeping, stacker, vertical warehouse zero picking area, 1 st picking and warehouse discharging; warehousing, vertical warehouse storage area protection, stacker, 2 nd forklift, flat warehouse zero picking area, 2 nd picking and warehouse discharging; warehousing, a flat warehouse zero picking area, 2 nd picking and ex-warehouse; warehousing, erecting a zero-picking area, 1 st picking and delivering; and warehousing, a flat warehouse storage area, a 1 st forklift, a flat warehouse zero-picking area, a 2 nd picking and ex-warehouse.

Preferably, in the commodity flow direction traffic determination device according to the second aspect, the node information table includes a node name of each node, a node function indicating whether the node is a storable node, an upper limit of storage capacity, a storage cost, a traffic capacity online, and a traffic cost.

Preferably, in the article flow direction flow rate determining device of the second aspect, the article information table includes an article name of each article, a unit volume of the article, a low stock/high stock number of the article, and an article sales amount.

Preferably, in the commodity flow direction and flow rate determining device according to the second aspect, the storage policy table includes a commodity name, a node name, and a storage requirement of each commodity, the storage requirement of each commodity includes a minimum number of storages, a maximum number of storages, and a number of turnaround days in the case of stock, and the storage requirement is a number greater than zero or is empty, and when the storage requirement is empty, it indicates that there is no constraint on the storage requirement.

The present invention also provides an electronic device for determining a flow direction of goods entering and/or leaving a warehouse, including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method according to the first aspect.

The invention also provides a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method according to the first aspect.

One embodiment of the above invention has the following advantages or benefits: due to the adoption of the technical means of planning the proper storage area, the flow and the flow direction of the warehouse entry and the warehouse exit for each goods through the network flow model, the overall cost can be optimized on the premise of meeting the demands of the warehouse entry and the warehouse exit and the storage business, and the overall efficiency is improved.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

fig. 1 is a schematic diagram of a basic flow of a goods flow direction flow rate determination method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of main modules of an article flow direction determining apparatus according to an embodiment of the present invention;

fig. 3 is an example of a flow chart of a specific flow of the goods flow direction flow rate determination method according to the embodiment of the present invention;

fig. 4 is an example of a network configuration diagram in the goods flow direction flow rate determination method according to the embodiment of the present invention;

fig. 5 is another example of a network configuration diagram in the goods flow direction flow rate determination method according to the embodiment of the present invention;

FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;

fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram of a main flow of an article flow direction flow determination method for warehousing and/or warehousing articles to and/or from a warehouse according to an embodiment of the present invention. According to the method for determining the flow direction and the flow rate of the goods, the overall cost is optimized and the overall efficiency is improved on the premise of meeting the business requirements of warehousing and storage through an intelligent process.

As shown in fig. 1, the basic flow of the flow direction and flow rate determining method for warehousing and ex-warehousing goods according to the present embodiment includes steps S101 to S104. In step S101, a network structure table is generated, and related data of the goods and the warehouse and a configuration parameter table are obtained. In step 102, based on the result in step S101, a network flow model and its constraints and targets are generated. In step S103, a solver is used to model-solve the network flow model. Finally, in step S104, the goods flow direction and flow rate distribution of the goods is determined based on the solution result in step S103.

Specifically, in the above step S101, acquiring the basic attribute data of the goods includes, for example, acquiring information data of the goods, warehousing and warehousing of the goods in a predetermined period of time in the past, and stock data; and obtaining warehouse data includes, for example, obtaining one or more of a storage capacity, a storage cost, a traffic capacity, and a traffic cost of the warehouse; and the configuration parameter table is generated by, for example, manually inputting configuration parameters and the like, and includes, for example, one or more of a storage period, a predetermined unit of operating time, a storage volume coefficient, and a period cost coefficient, and corresponding parameter values.

In step S101, representing each area, device, or work flow of the warehouse as a network configuration diagram including nodes and directed edges may be accomplished, for example, as follows.

Abstracting each area, equipment or operation flow of the warehouse into nodes in the network, abstracting the relationship among the areas, the equipment or the operation flows into directed edges in the network, and abstracting the processes of warehousing, storing and exporting goods into flows flowing in the network. For example, a warehouse is composed of two areas, i.e., a person-to-goods picking area and an AGV person-to-person picking area, both areas can be directly warehoused and warehoused, and the network structure diagram can be abstracted into the network structure diagram shown in fig. 4. For another example, a warehouse is composed of a vertical warehouse area and a warehouse area, the vertical warehouse area and the warehouse area both include two areas, namely a zero-picking area and a storage area, four areas can be directly warehoused, goods can be delivered from the vertical warehouse zero-picking area and the vertical warehouse zero-picking area, the vertical warehouse storage area can be used for replenishing goods to the two zero-picking areas, the horizontal warehouse storage area can only be used for replenishing goods to the horizontal warehouse zero-picking area, and then the network structure diagram can be abstracted into the network structure diagram shown in fig. 5.

It should be noted that "warehousing" and "ex-warehouse" in the network structure diagram are two virtual nodes, and when the network structure is abstracted, all the flows do not need to be modeled, but only important (or bottleneck) flows are concerned, taking fig. 4 as an example, sorting, packaging and other flows may be left after the picking step, but the flows of non-concerned points of sorting, packaging and the like are not embodied in the network structure diagram.

When the network structure diagram is abstracted, the source node connected by a directed edge and the corresponding destination node can be clarified, so that a network structure table can be correspondingly generated, the network structure table is composed of two columns of the source node and the destination node, each row indicates that a directed edge connecting the source node and the destination node exists from the source node to the destination node, and goods can flow from the source node to the destination node.

Using the above-obtained various data and the network structure diagram that has been established, a network flow model for the warehousing and/or ex-warehousing of goods and the constraints and targets of the network flow model may be generated in step S102. More specifically, in order to more clearly utilize the acquired various types of the above-mentioned data, for example, four tables may be generated by type for the various collected data before step S102, and specifically, in combination with the warehouse data, a node information table is generated, which shows at least node names for the nodes included in the network structure table and node functions indicating whether or not the nodes can be stored; generating an item information table showing basic attribute data of each item according to the basic attribute data of the item; generating a storage strategy table showing the storage strategy of each goods at the corresponding node name in the node information table according to the storage requirement data of the goods, and generating a configuration parameter table showing the parameter name and the corresponding parameter value according to preset configuration parameter data. And generating a table, the node information table, the goods information table, the storage strategy table and the configuration parameter table according to the network structure diagram, and generating the network flow model and the constraint and target of the network flow model.

In step S103, model solution may be performed by using a solver such as SCIP, Cplex, Gurobi, and in step S104, the stock distribution (the number of safe stocks and the number of circulating stocks of each item at each storable node) and the traffic simulation condition are determined based on the calculation result in step S103 and output in the form of, for example, a table or the like.

In step S101, the flow direction and flow rate method of the goods according to the present invention will be described more clearly with reference to an example of the flowchart of fig. 3, taking an example that the abstraction of each area, device, or workflow of the warehouse as a node in the network, the abstraction of the relationship between the areas, devices, or workflows as a directed edge in the network, and the abstraction of the processes of warehousing, storing, and exporting the goods as a flow flowing in the network is completed in the example shown in fig. 4.

As shown in fig. 3, first, each area, device and operation flow of the warehouse are abstracted into a network structure diagram shown in fig. 4, including each node of warehousing, person-to-stock storage, person-to-stock picking and ex-warehouse; and each node for warehousing, goods-to-person storage, goods-to-person picking and ex-warehousing.

Then, a network-structure table is generated according to the generated network-structure table, as shown in table 1 below, the network-structure table is composed of two columns of source nodes and destination nodes, each row indicates that a directed edge connecting the source nodes and the destination nodes exists from the source nodes to the destination nodes, i.e., goods can flow from the source nodes to the destination nodes.

[ Table 1]

Source node	Destination node
		Put in storage	Storage of goods arrived by people
Put in storage	Person-to-person storage
		Storage of goods arrived by people	Person-to-goods picking
Person-to-person storage	Person-to-person picking
		Person-to-goods picking	Delivery from warehouse
Person-to-person picking	Delivery from warehouse

In addition, as shown in fig. 3, various data related to the goods, the warehouse (warehouse facilities, personnel), and the configuration parameters may be acquired. In the specific flow, goods information data, and warehousing and inventory data of a past period T (such as one month) are acquired; and acquiring relevant data of equipment (personnel) capacity, efficiency, cost and the like.

Then, a node information table site _ info, a good information table product _ info, a storage policy table inventorys _ policies, and a configuration parameter table config are generated based on the acquired data and the generated network structure table, wherein the configuration parameter table config may be generated by manual input.

< node information Table >

The node information table contains basic attributes of each node, and at least contains information such as node name, node function (whether the node can be stored), storage capacity upper limit, storage cost, flow capacity online, flow cost and the like. In addition to the node name and node function, other information may be defaulted, such that there is no upper limit on the default storage or traffic capabilities, no storage or traffic costs. Table 2 below is a non-limiting example of a node information table generated for each node.

[ Table 2]

< creation of article information Table >

The item information table contains basic attributes of each item, and should contain at least information of an item name, a unit volume of the item, a low stock/high stock number of the item, such as daily average sales, and the like. Table 3 below is one non-limiting example of an item information table.

[ Table 3]

< creation of storage policy Table >

One record of the storage strategy table indicates that a certain goods can be stored in a certain node, and in addition, the storage requirements, such as the minimum storage number in the case of stock, the turnover days and the like, can be input, or the storage strategy table can be unfilled, so that no corresponding constraint exists. Table 4 below is one non-limiting example of a storage policy table:

[ Table 4]

< configuration parameter Table >

The configuration parameter table may be a table input by a worker, and includes parameters such as a storage period, a predetermined unit of working time, a storage volume coefficient, and a period cost coefficient. Here, the storage volume coefficient is a percentage coefficient smaller than 1 which is manually input, and since the number of circulating stocks of different items at the node position is different and the volume thereof is different, not every item is the storage stock which occupies the highest at the corresponding node, when calculating the model constraint, for example, when setting "stock occupancy satisfies condition" in the second stage described later, it is preferable to set the storage volume coefficient not using the highest storage volume but multiplying the storage volume coefficient set in advance. In addition, the cycle cost coefficient here is also a percentage coefficient smaller than 1, which is manually input, and due to different storage cycles of different goods, it may not be possible that every goods is stored according to the set cycle, for example, the storage cycle in the following table is set to 30 days, but it is likely that storage cycles of some goods are subsequently 50 days, 80 days, and so on, at this time, if the cycle cost coefficient set by this person is multiplied, the cost calculation can be approximately converted into a fixed storage cycle, and the establishment of the model constraint is more accurate.

Table 5 below is a non-limiting example of a configuration parameter table, for example, the storage period is selected as the number of stored days in a cycle, and is set to 30 days as one storage period; the predetermined unit of the operation time length is selected as the operation time length per day and set to 12 hours; the storage volume factor is set to 0.6 and the cycle cost factor is set to 0.2.

[ Table 5]

Parameter name	Parameter value
		Days of the cycle (day)	30
Working time per day (h)	12
		Coefficient of storage volume	0.6
Cost factor of cycle	0.2

As shown in the specific flowchart of fig. 3, the constraints and targets of the network flow model are generated according to the generated network structure table network-structure, node information table site _ info, goods information table product _ info, storage policy table inventoryy _ policies, and configuration parameter table config.

In this example, the generation of the constraints and objectives for the network flow model may specifically include the following steps.

< description of the model >

The model is divided into three phases, the first phase (horizon1) flowing into safety stock, the second phase (horizon2) flowing into and out of turnaround stock and meeting the demand for in-cycle sales volume, and the third phase (horizon3) flowing out of safety stock. The model can determine the stock distribution, the flow and each cost of each goods in the normal state by integrating three-stage constraints.

< calculation model parameters (including symbolic explanation) >

ALL _ SITES is the set of ALL nodes;

ALL _ INVENTORY _ SITES: all allowed node sets to be stored;

ALL _ NONE _ innovative _ sizes: all the nodes which are not allowed to be stored are collected;

IB: a virtual warehousing node;

OB is a virtual ex-warehouse node;

ALL _ DIRECT _ involved _ SITES: the node set can be directly used as the storage in a warehouse (namely, the storage node is directly connected with the IB, and the intermediate node can be the device node and the process node which do not allow storage)

Next (site): a set of relay nodes for the site of the node;

prev (site): a successor node set of the node site;

floutlim (site): flow limitation of the node site;

flout _ cost (site): each piece of processing cost of the node site;

invlim (site): capacity limit of the node site;

inv _ cost (site): cost per unit volume of the nodal site;

ALL _ PRODUCTS: a collection of all items;

sale _ qty (product) sales demand in the product cycle, sale _ qty (product) daily average sales of the product per cycle days;

uni _ vol (product): unit volume of the good product;

turn _ times (product): the turnover frequency of the goods product in the cycle days can be understood as the warehousing frequency of the goods in the cycle days;

turn _ times (product, site): the maximum turnover number of the product of the goods in the node site can be calculated by using the number of days of the model cycle and the number of days of turnover of the goods in the table inventory _ policies;

discount: storing the volume factor;

lambda: a cycle cost factor.

< Generation of model variables >

an allow _ inv (product, site) variable of 0-1, indicating that the good product is stored at the node site when the value is 1, and indicating that the good product is not stored at the node site when the value is 0;

inv (product, site, horizon), the stock number of the final product of horizon at the node site;

saf _ inv (product, site) number of security stocks of the good product at the node site;

a cycle _ inv (product, site) number of cycle stocks of the good product at the node site;

flow (product, site1, site2, horizons) volume (number of pieces) of horizons good product from node site1 to node site 2.

< generative model constraints (the following formula is, for example, the latex format) >

A. First stage (horizons 1): the initial inventory is 0, and the inventory amount is the safe inventory amount;

the distribution of inventory at the end of the first phase is equal to the distribution of safety inventory

inv(product,site,horizon1)＝saf_inv(product,site)\forall product,site

The warehousing quantity is all safety stocks

\sum_{floutnode\in next(IB)}flow(product,IB,floutnode,horizon1)＝＝saf_inv(product)\forall product

The delivery volume is 0

\sum_{flinnode\in prev(OB)}flow(product,flinnode,OB,horizon1)＝＝0\forall product

Node flow balance (with stock node: end stock in term + outflow; without stock node: inflow: outflow)

An inventory node: "product, flinnode in PREV (site) } flow (product, flinnode, site, horizon1) ═ saf _ inv (product, site) +" product _ [ { flownnode in NEXT (site) } flow (product, site, horizon1) \\ ALL for product, site _ ALL _ invent _ SITES

No inventory node: "sum" { flinnode in PREV (site) } flow (product, flinnode, site, horizon1) } sum "{ flownnode in NEXT (site) } flow (product, site, flow, horizon1) } for ALL product, site \ in ALL _ NONE _ invent _ size

B. Second stage (horizons 2): the warehousing/ex-warehousing quantity is the sales volume demand of goods, and the initial and final inventory distribution is not changed;

the inventory distribution at the end of the second phase is equal to the inventory distribution at the end of the first phase

inv(product,site,horizon2)＝saf_inv(product,site)\forall product,site

The delivery volume is the sales demand of the goods

\sum_{site\in prev(OB)}flow_normal(product,site,OB,horizon2)＝＝sale_qty(product)\forall product

Node flow balance (inflow ═ outflow)

\sum_{flinnode in PREV(site)}flow(product,flinnode,site,horizon2)＝＝\sum_{floutnode in NEXT(site)}flow(product,site,floutnode,horizon2)\forall product,site

Upper bound of node traffic

\sum_{product\in ALL_PRODUCTS}\sum_{floutnode in NEXT(site)}(flow(product,site,floutnode,horizon2)+flow(product,site,floutnode,horizon2))<＝floutlim(site)\forall site

Node storage capacity upper limit constraint

All the turnover inventory amounts stored by the nodes capable of being directly warehoused are more than or equal to the single warehousing amount: flow (product, IB, site, horizon2) < ═ turn _ times (product) > cycle _ inv (product, site) \\ for product, site \ in ALL _ DIRECT _ involved _ innovation _ states;

for an inventory node, if a particular item combination can flow through the node in the second stage (Horizon2), the item combination must be in inventory at the node, and the total throughput of the item combination is less than or equal to the inventory of the combination at the node multiplied by the maximum number of turnover permitted for the item combination at the node: "sum { flow node \ in NEXT (site) } flow (product, site, flow node, horizon2) < ═ turn _ times (product, site) } cycle _ inv (product, site);

the occupied volume of the stock meets the condition: \\ \ _;

other storage related constraints

If some items are not allowed to be held at some nodes: allow _ inv (product, site) ═ 0;

if a certain goods combination is stored in a certain node, the requirement of minimum and maximum storage number can be met, M (product, site) allow _ inv (product, site) or M (product, site) allow _ inv (product, site); if M (product, site) and M (product, site) are not given, default M (product, site) ═ 0, M (product, site) is a positive number large enough;

C. third stage (horizons 3): the ex-warehouse quantity is the safe stock quantity, and the end-of-term stock is 0;

end of stage three inventory is 0

inv(product,site,horizon3)＝0\forall product,site

The delivery amount is all safety stock

\sum_{flinnode\in prev(OB)}flow(product,flinnode,OB,horizon3)＝＝saf_inv(product)\forall product

Node flow balance (with stock node: initial stock + stock in ═ outflow; without stock node: inflow ═ outflow)

An inventory node: "sum" { flinnode in PREV (site) } flow (product, flinnode, site, horizon3) + saf _ inv (product, site) ═ sum "{ flow in NEXT (site) } flow (product, site, flow, horizon3) \\ force product, site \ in ALL _ innovation _ SITES

No inventory node: "sum" { flinnode in PREV (site) } flow (product, flinnode, site, horizon3) ═ sum \ { flownnode in NEXT (site) } flow (product, site, flow, horizon3) } for ALL product, site \ in ALL _ NONE _ invent _ SITES.

< generative model object >

The optimization objective of the model is to minimize the total cost, including storage costs and job costs;

Minimize(total_flow_cost+total_inv_cost)

the storage cost is the sum of the stock occupation costs of goods of all nodes allowing storage:

total_inv_cost＝＝\sum_{site\in ALL_INVENTORY_SITES}(\sum_{product\in ALL_PRODUCTS}(saf_inv(product,site)+discount*cycl_inv(product,site))*uni_vol(product))*inv_cost(site))

the operation cost is the sum of the goods flow-through cost of all nodes in the second stage and the third stage:

total_flow_cost＝＝\sum_{site in ALL_SITES}\sum_{product in ALL_PRODUCTS}\sum_{floutnode\in NEXT(site)}flow(product,site,floutnode,horizon2)*flow_cost(site)

+\sum_{site in ALL_SITES}\sum_{product in ALL_PRODUCTS}\sum_{floutnode\in NEXT(site)}flow(product,site,floutnode,horizon3)*flow_cost(site)*lambda。

after the constraints and targets of the network flow model are generated, for example, in the manner as described above, as shown in fig. 3, model solution is performed, where model solution is performed using a solver such as SCIP, Cplex, Gurobi, etc., resulting in stock distribution (the number of safe stocks and the number of circulating stocks of each item at each storable node) and traffic simulation conditions, and then the flow-to-traffic distribution of the resolved items is output, for example, in a table.

In addition, it should be noted that if the model is found to be infeasible in the solution process, the user can check the connectivity of the input network structure and whether the data in each table is correct to solve the problem.

The first embodiment and the more specific example of the embodiments of the present invention have been described in detail above, and by the method and the apparatus for determining the flow direction and flow rate of goods, each area, device, and operation flow of a warehouse are abstracted into a network graph formed by nodes and directed edges, and a suitable storage area, flow rate and flow direction of entering and exiting the warehouse are planned for each goods through a network flow model, so that the overall cost is optimized on the premise of meeting the demands of entering and exiting the warehouse and storing services, and the overall efficiency is improved.

In addition, it should be noted that the selection of each node, various data, and the like in the description of the example of the specific flow performed with reference to fig. 3 are only specific selections when the network structure diagram is shown in fig. 4, and are not restrictive, and may be reasonably selected as needed. In practice, the corresponding specific flow processing may be performed in an abstract form of the network configuration diagram, for example, in the form shown in fig. 5.

According to a second aspect of the embodiments of the present invention, there is provided an apparatus for implementing the above method.

Fig. 2 is a schematic diagram of main blocks of the article flow direction and flow rate determining apparatus according to the embodiment of the present invention, and as shown in fig. 2, the article flow direction and flow rate determining apparatus 200 includes: a network structure table generating module 201, a data acquiring module 202, a network flow model generating module 203 and a calculating module 204.

The network structure table generating module 201 generates a network structure table including a source node and a corresponding destination node by representing the processes of warehousing, storing and exporting the goods as a network structure diagram composed of nodes and directed edges.

The data obtaining module 202 obtains predetermined warehouse data, basic attribute data of the goods, storage requirement data of the goods, and preset configuration parameter data. The data obtaining module 202 may also generate a node information table site _ info, a good information table product _ info, a storage policy table inventoryy _ policies, and a configuration parameter table config after obtaining each data, as shown in fig. 3 of the first aspect of the above embodiments.

The network flow model generation module 203 generates a network flow model according to the information of each node in the network structure table generated by the network structure table generation module 201, and the warehouse data, the basic attribute data of the goods, the storage requirement data of the goods, and the configuration parameter data acquired by the data acquisition module 202, and generates constraints and targets of the network flow model.

The calculation module 204 performs model solution on the network flow model based on the constraints and objectives of the network flow model, and further determines the flow direction and flow distribution of the goods.

The network flow model generation module 203 may perform the following operations corresponding to the method of the first aspect: and dividing the generated network flow model into a first stage to a third stage, wherein the first stage flows into the safety stock, the second stage flows into and out of the turnover stock and meets the demand of sales volume in a period, and the third stage flows out of the safety stock, calculates model parameters of the model, generates model variables of the model, generates model constraints of the model and generates a model target of the model.

Fig. 6 illustrates an exemplary system architecture 600 of a commodity flow direction determining method or device to which embodiments of the present invention may be applied.

As shown in fig. 6, the system architecture 600 may include

terminal devices

601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the

terminal devices

601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The

terminal devices

601, 602, 603 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 605 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the

terminal devices

601, 602, 603. The background management server may analyze and otherwise process the received data such as the goods information query request, and feed back the processing result (e.g., the target push information, the goods information — just an example) to the terminal device.

It should be noted that the method for determining the flow direction of the goods provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the device for determining the flow direction of the goods is generally disposed in the server 605.

It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the disclosed embodiments include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program articles according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a network structure table generation module, a data acquisition module, a network flow model generation module, and a computation module. For example, the network structure table generation module may also be described as a "module for generating a network structure table including a source node and a corresponding destination node by representing processes of warehousing, storing and exporting goods as a network structure diagram composed of nodes and directed edges".

As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: the processes of warehousing, storing and ex-warehousing goods are represented as a network structure diagram consisting of nodes and directed edges, so as to generate a network structure table comprising source nodes and corresponding destination nodes,

According to the technical scheme of the embodiment of the invention, each area, equipment and operation flow of the warehouse are abstracted into a network graph formed by nodes and directed edges, and a suitable storage area, warehouse-in and warehouse-out flow and flow direction are planned for each goods through a network flow model, so that the overall cost is optimized on the premise of meeting the demands of warehouse-in and warehouse-out and storage services, the overall efficiency is improved, and intelligent recommendation is realized.

The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining the flow direction and the flow rate of goods is characterized by comprising the following steps:

2. The commodity flow direction flow rate determining method according to claim 1,

before generating the constraints and targets of the network flow model, the method further comprises the following steps:

3. The commodity flow direction flow rate determining method according to claim 2,

the generation of the constraints and objectives of the network flow model comprises the following steps:

the network flow model is divided into a first stage, a second stage and a third stage, wherein the first stage flows into the safety stock, the second stage flows into and out of the turnover stock and meets the demand of sales in a period, the third stage flows out of the safety stock,

the model parameters of the model are calculated,

the model variables of the model are generated and,

model constraints for generating a model, an

A model object of the model is generated.

4. The commodity flow direction flow rate determining method according to claim 1,

acquiring basic attribute data of the goods comprises acquiring information data of the goods and warehousing and inventory data of the goods in a past preset time period; and is

Obtaining the warehouse data includes obtaining one or more of a storage capacity, a storage cost, a traffic capacity, and a traffic cost of a warehouse.

5. The commodity flow direction flow rate determining method according to claim 1,

the configuration parameter table comprises one or more items of storage period, working time of a preset unit, a storage volume coefficient and a period cost coefficient and corresponding parameter values.

6. The commodity flow direction flow rate determining method according to claim 1,

generating the network structure diagram by using the following nodes which are sequentially connected by directed edges: warehousing, storing, picking and delivering goods; warehousing, goods-to-person storage, goods-to-person selection and ex-warehouse; or

7. The commodity flow direction flow rate determining method according to claim 2,

the node information table includes the node name of each node, the node function indicating whether the node is a storable node, the upper limit of the storage capacity, the storage cost, the flow capacity online and the flow cost.

8. The commodity flow direction flow rate determining method according to claim 2,

the goods information table contains the goods names of the goods, the unit volume of the goods, the low storage quantity/high storage quantity of the goods and the goods sales quantity.

9. The method of optimizing commodity flow direction and flow rate according to claim 2,

the storage strategy table comprises the goods name, the node name and the storage requirement of each goods,

the storage requirements of the goods include a minimum number of stored items in stock, a maximum number of stored items, a turnaround time, and

the storage requirement is a number greater than zero or null, and when the storage requirement is null, no constraint on the storage requirement is represented.

10. A product flow direction and flow rate determining apparatus, comprising:

a network structure table generation module: the method comprises the steps of representing the processes of warehousing, storing and ex-warehousing of goods as a network structure diagram consisting of nodes and directed edges, and further generating a network structure table comprising source nodes and corresponding destination nodes;

11. The commodity flow direction flow rate determining apparatus according to claim 10,

the network flow model generation module performs the following operations:

the model parameters of the model are calculated,

the model variables of the model are generated and,

model constraints for generating a model, an

A model object of the model is generated.

12. An electronic device for flow determination of a flow direction of goods to and/or from a warehouse, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.

13. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.